Abstract: A system and method are disclosed including a digital hub and a cloud database. The digital hub encodes, as a virtual supply chain network, the structure, one or more objectives, and one or more states of a supply chain network and contextualizes data received from one or more supply chain entities and one or more external data sources with the virtual supply chain network. The digital hub further employs machine learning to extract insights from the contextualized data and monitors external data sources for an event that may impact the one or more objectives of the supply chain network. Responsive to identifying an event that may impact the one or more objectives, the digital hub automatically adjusts one or more of robotic warehouse systems, robotic inventory systems, automated guided vehicles, mobile racking units, automated robotic production machinery, and robotic devices.

Abstract: Implementations generally relate to a method of matching job-seekers and employers in real-time with low required effort by the job-seekers and employers. A system-wide taxonomy is used in matching job-seekers and employers, thereby reducing the time for the recruiting process. Employers create a job position, and specify the relative importance of traits associated with the job position. Job-seekers answer profile questions (e.g. work experience), and the system uses the taxonomy to generate a list of job-seeker skills based on the answers to these questions. A list of job-seekers is then generated for the job position based on a mapping of the available job-seekers' skills and the employer rankings of the job position traits. A number of additional factors, like job seekers' in-app engagement, are used to further sort and filter the list of job-seekers.

Abstract: A system and method are disclosed including a digital hub and a cloud database. The digital hub encodes, as a virtual supply chain network, the structure, one or more objectives, and one or more states of a supply chain network and contextualizes data received from one or more supply chain entities and one or more external data sources with the virtual supply chain network. The digital hub further employs machine learning to extract insights from the contextualized data and monitors external data sources for an event that may impact the one or more objectives of the supply chain network. Responsive to identifying an event that may impact the one or more objectives, the digital hub automatically adjusts one or more of robotic warehouse systems, robotic inventory systems, automated guided vehicles, mobile racking units, automated robotic production machinery, and robotic devices.

Abstract: Implementations generally relate to a method of matching job-seekers and employers in real-time with low required effort by the job-seekers and employers. A system-wide taxonomy is used in matching job-seekers and employers, thereby reducing the time for the recruiting process. Employers create a job position, and specify the relative importance of traits associated with the job position. Job-seekers answer profile questions (e.g. work experience), and the system uses the taxonomy to generate a list of job-seeker skills based on the answers to these questions. A list of job-seekers is then generated for the job position based on a mapping of the available job-seekers' skills and the employer rankings of the job position traits. A number of additional factors, like job seekers' in-app engagement, are used to further sort and filter the list of job-seekers.